Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 10-15, 17-19, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by DAI et al. (US 2014/0293904 A1), hereinafter DAI.
Regarding claim 1, DAI discloses a method of operating a control device (central processor (CP) 106, see figure 1), the method comprising:
obtaining a transmission power coefficient and a codebook parameter (network utility function and system resource) based on a data rate of each of a plurality of user equipment, a transmission power of a plurality of base stations and a fronthaul capacity limit (a multiple-input multiple-output network 100 comprising of a plurality of BSs 102 connected to central processor 106, see figure 1; dynamically and adaptively forming, by a cloud central processor, a cluster of transmission points (TPs) for use in transmit beamforming for each of a plurality of user equipment (UEs) in the system by optimizing a network utility function and system resources, determining, by the cloud central processor, a sparse beamforming vector for each user equipment according to the forming the cluster, see ¶ 0025);
generating a transmission signal based on the transmission power coefficient (transmitting, by the cloud central processor, a message and first beamforming coefficients to ones of the transmission points that form the cluster of TPs for a first user equipment, wherein the ones of the transmission points that form the cluster of TPs for the first user equipment correspond to nonzero entries in a first beamforming vector corresponding to a first user equipment… the utility function is a weighted rate sum of user rates and wherein the pre-specified system resources constraints include transmit power constraints and backhaul rate constraints, see ¶ 0025); and
transmitting the transmission signal to the plurality of base stations through a fronthaul (transmitting, by the cloud central processor, a message and first beamforming coefficients to ones of the transmission points that form the cluster of TPs for a first user equipment, see ¶ 0025).
Regarding claim 2, DAI discloses the obtaining the transmission power coefficient and the codebook parameter comprises: calculating the transmission power coefficient based on an initial codebook parameter; and calculating the codebook parameter based on the calculated transmission power coefficient, wherein the transmission power coefficient and the codebook parameter are calculated by maximizing a minimum value of the data rate of each of the plurality of user equipment, and limiting the transmission power of the plurality of base stations to be is less than or equal to a transmission power limit and satisfy the fronthaul capacity limit (forming the cluster includes iteratively optimizing, by the cloud central processor, one of a first function and a second function, wherein iteratively optimizing the first function includes iteratively minimizing required system resources to support at least one desired user experience constraint, and wherein iteratively optimizing the second function includes iteratively maximizing a utility function of user transmission rates with pre-specified system resource constraints, wherein the system includes a plurality of transmission points (TPs) and a plurality of user equipment. In an embodiment, the utility function is a weighted rate sum of user rates and wherein the pre-specified system resources constraints include transmit power constraints and backhaul rate constraints, see ¶ 0025).
Regarding claim 3, DAI discloses the processor further configured to: set a minimum comparison value by using a bisection method (the utility maximization problem solvable through a generalized weighted minimum mean square error (WMMSE) approach, see ¶ 0022); and calculate the transmission power coefficient by using the minimum comparison value and a convex solver (solve using standard convex optimization solver such as CVX, see ¶ 0050).
Regarding claim 4, DAI discloses the at least one processor further configured to: re-calculate the transmission power coefficient based on the calculated codebook parameter; and re-calculate the codebook parameter based on the re-calculated transmission power coefficient (a method and system to iteratively approximate the per-BS backhaul constraints using a weighted l.sub.1-norm technique and reformulate the backhaul constraints as weighted per-BS power constraints. This approximation allows one to solve the weighted sum rate maximization problem iteratively through a generalized weighted minimum mean square error (WMMSE) approach… iterative link removal and iterative user pool shrinking, which dynamically decrease the potential BS cluster size and user scheduling pool. Numerical results show that the disclosed methods and systems can significantly improve the system throughput as compared to the nave BS clustering strategy based on the channel strength, see ¶ 0023).
Regarding claim 10, DAI discloses the transmission signal is transmitted to the plurality of base stations through the fronthaul generating fronthaul noise corresponding to the codebook parameter (the system resources are a weighted sum of the transmit powers and the backhaul rates. In an additional embodiment, the one or more user experience constraints are selected from the group consisting of signal plus interference to noise ratio (SINR), data rate, and a combination thereof, see ¶ 0024).
Regarding claim 11, DAI discloses a control device comprising: at least one memory storing one or more instructions; and at least one processor configured to execute the one or more instructions:
obtain a transmission power coefficient and a codebook parameter (network utility function and system resources) based on a data rate of each of a plurality of user equipment, a transmission power of a plurality of base stations and a fronthaul capacity limit (a multiple-input multiple-output network 100 comprising of a plurality of BSs 102 connected to central processor 106, see figure 1; dynamically and adaptively forming, by a cloud central processor, a cluster of transmission points (TPs) for use in transmit beamforming for each of a plurality of user equipment (UEs) in the system by optimizing a network utility function and system resources, determining, by the cloud central processor, a sparse beamforming vector for each user equipment according to the forming the cluster, see ¶ 0025);
generate a transmission signal based on the transmission power coefficient (transmitting, by the cloud central processor, a message and first beamforming coefficients to ones of the transmission points that form the cluster of TPs for a first user equipment, wherein the ones of the transmission points that form the cluster of TPs for the first user equipment correspond to nonzero entries in a first beamforming vector corresponding to a first user equipment… the utility function is a weighted rate sum of user rates and wherein the pre-specified system resources constraints include transmit power constraints and backhaul rate constraints, see ¶ 0025); and
transmit the transmission signal to the plurality of base stations through a fronthaul (transmitting, by the cloud central processor, a message and first beamforming coefficients to ones of the transmission points that form the cluster of TPs for a first user equipment, see ¶ 0025).
Regarding claim 12, DAI discloses the processor further configured to: set a minimum comparison value by using a bisection method (the utility maximization problem solvable through a generalized weighted minimum mean square error (WMMSE) approach, see ¶ 0022); and calculate the transmission power coefficient by using the minimum comparison value and a convex solver (solve using standard convex optimization solver such as CVX, see ¶ 0050).
Regarding claim 13, DAI discloses calculate the transmission power coefficient based on an initial codebook parameter; and calculate the codebook parameter based on the calculated transmission power coefficient (initial the weighting factor 402 and the update the weighting factor 410, see figure 4).
Regarding claim 14, DAI discloses the at least one processor further configured to: re-calculate the transmission power coefficient based on the calculated codebook parameter; and re-calculate the codebook parameter based on the re-calculated transmission power coefficient (a method and system to iteratively approximate the per-BS backhaul constraints using a weighted l.sub.1-norm technique and reformulate the backhaul constraints as weighted per-BS power constraints. This approximation allows one to solve the weighted sum rate maximization problem iteratively through a generalized weighted minimum mean square error (WMMSE) approach… iterative link removal and iterative user pool shrinking, which dynamically decrease the potential BS cluster size and user scheduling pool. Numerical results show that the disclosed methods and systems can significantly improve the system throughput as compared to the nave BS clustering strategy based on the channel strength, see ¶ 0023).
Regarding claim 15, DAI discloses the at least one processor further configured to, after re-calculating the codebook parameter, determine whether to re-calculate the transmission power coefficient and the codebook parameter based on at least one of a variation of the transmission power coefficient due to the re-calculating of the transmission power coefficient and a variation of the codebook parameter due to the re-calculating of the codebook parameter (a method and system to iteratively approximate the per-BS backhaul constraints using a weighted l.sub.1-norm technique and reformulate the backhaul constraints as weighted per-BS power constraints. This approximation allows one to solve the weighted sum rate maximization problem iteratively through a generalized weighted minimum mean square error (WMMSE) approach… iterative link removal and iterative user pool shrinking, which dynamically decrease the potential BS cluster size and user scheduling pool. Numerical results show that the disclosed methods and systems can significantly improve the system throughput as compared to the nave BS clustering strategy based on the channel strength, see ¶ 0023).
Regarding claim 17, DAI discloses the transmission signal is transmitted to the plurality of base stations through the fronthaul generating fronthaul noise corresponding to the codebook parameter (the system resources are a weighted sum of the transmit powers and the backhaul rates. In an additional embodiment, the one or more user experience constraints are selected from the group consisting of signal plus interference to noise ratio (SINR), data rate, and a combination thereof, see ¶ 0024).
Regarding claim 18, DAI discloses the transmission power coefficient and the codebook parameter are calculated by maximizing a minimum value of the data rate of each of the plurality of user equipment, and limiting the transmission power of the plurality of base stations to be is less than or equal to a transmission power limit and satisfy the fronthaul capacity limit (forming the cluster includes iteratively optimizing, by the cloud central processor, one of a first function and a second function, wherein iteratively optimizing the first function includes iteratively minimizing required system resources to support at least one desired user experience constraint, and wherein iteratively optimizing the second function includes iteratively maximizing a utility function of user transmission rates with pre-specified system resource constraints, wherein the system includes a plurality of transmission points (TPs) and a plurality of user equipment. In an embodiment, the utility function is a weighted rate sum of user rates and wherein the pre-specified system resources constraints include transmit power constraints and backhaul rate constraints, see ¶ 0025).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 9, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over DAI in view of ZANGI (US 2010/0081399 A1), hereinafter ZANGI.
Regarding claims 9, 16, DAI fails to disclose wherein the transmission signal is generated based on a data signal to be transmitted to each of the plurality of user equipment, the obtained transmission power coefficient, and a linear precoder.
In the same field of endeavor, ZANGI discloses a wireless network which participates in radio frequency communication with plural wireless terminals. The network comprises plural transmitters; a precoder value processor configured to develop a set of precoder values; and a precoder which uses the precoder values for coding the signals transmitted from the plural transmitters (see ¶ 0014).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement ZANGI’s teaching in the network taught by DAI for the linear precoder provide signal processing techniques used in multi-antenna (MIMO) wireless systems to manage interference and improve signal quality by applying a linear transformation to the data before transmission.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over DAI in view of ZHOU et al. (US 2016/0013850 A1), hereinafter ZHOU.
Regarding claim 19, DAI discloses a method of operating a communication system including a central control device (central processor 106), a fronthaul (link connecting the central processor 106 and the plurality of base stations 102, see figure 1), and a plurality of base stations (base stations 102, see figure 1), the method comprising:
obtaining a transmission power coefficient and a codebook parameter based on a data rate of each of a plurality of user equipment, a transmission power of a plurality of base stations and a fronthaul capacity limit (a multiple-input multiple-output network 100 comprising of a plurality of BSs 102 connected to central processor 106, see figure 1; dynamically and adaptively forming, by a cloud central processor, a cluster of transmission points (TPs) for use in transmit beamforming for each of a plurality of user equipment (UEs) in the system by optimizing a network utility function and system resources, determining, by the cloud central processor, a sparse beamforming vector for each user equipment according to the forming the cluster, see ¶ 0025);
generating a transmission signal based on the transmission power coefficient (transmitting, by the cloud central processor, a message and first beamforming coefficients to ones of the transmission points that form the cluster of TPs for a first user equipment, wherein the ones of the transmission points that form the cluster of TPs for the first user equipment correspond to nonzero entries in a first beamforming vector corresponding to a first user equipment… the utility function is a weighted rate sum of user rates and wherein the pre-specified system resources constraints include transmit power constraints and backhaul rate constraints, see ¶ 0025); and
transmitting the transmission signal to the plurality of base stations through a fronthaul (transmitting, by the cloud central processor, a message and first beamforming coefficients to ones of the transmission points that form the cluster of TPs for a first user equipment, see ¶ 0025)
DAI fails to disclose generating fronthaul noise corresponding to the codebook parameter; quantizing, by each of the plurality of base stations, the transmission signal received through the fronthaul; and transmitting, by the plurality of base stations, the quantized transmission signal to the plurality of user equipment, respectively.
In the same field of endeavor, ZHOU discloses a method for determining a transmit beamformer and a quantization noise covariance matrix for MIMO communications in a C-RAN includes obtaining, by a CP, channel state information for a MD being served by a plurality of BSs in the C-RAN, and generating a channel gain matrix in accordance with the channel state information… calculating separately a transmit beamforming vector for the MD and a quantization noise covariance matrix for the BSs by applying an approximation algorithm to solve the weighted sum-rate maximization model (see ¶ 0005-007).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to implement ZHOU’s teaching in the network taught by DAI for providing efficient schemes that can take advantage of the C-RAN architecture to improve overall communications performance and mitigate the inter-cell interference between the cells.
Allowable Subject Matter
Claims 5-8, 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 5-8, 20 are considered allowable over the prior art of record since the prior art of record fails to show or fairly suggest the obtaining the transmission power coefficient and the codebook parameter comprises wherein the obtaining the transmission power coefficient and the codebook parameter comprises, after re-calculating the codebook parameter, determining whether to re-perform the calculating of the transmission power coefficient and the calculating of the codebook parameter based on at least one of a variation of the transmission power coefficient due to the re-calculating of the transmission power coefficient and a variation of the codebook parameter due to the re-calculating of the codebook parameter.
Conclusion
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bob A. Phunkulh whose telephone number is (571) 272-3083. The examiner can normally be reached on Monday-Thursday from 8:00 A.M. to 5:00 P.M. (first week of the bi-week) and Monday-Friday (for second week of the bi-week).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor CHARLES C. JIANG can be reach on (571) 270-7191.
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/BOB A PHUNKULH/Primary Examiner, Art Unit 2412
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